Shuffled Frog Leaping Algorithm Research Based Optimal Iterative Learning Control

Abstract: To solve the problems of nonlinear and input constraints in the iterative learning control system, using real-coded shuffled frog leaping algorithm to solve optimization problem1 in iterative learning control,.A shuffled frog leaping algorithm(SFLA) based optimal iterative learning control is proposed. The algorithm combines the advantages of memetic algorithm and particle swarm optimization to simplify the algorithm of parameter selection, reduce the search space and improve the convergence rate. The proposed approach benefits from the design of a low-pass FIR filter. This filer successfully removes unwanted high frequency components of the input signal, which are generated by SFLA algorithm method due to the random nature of SFLA algorithm search. Simulation are used to illustrate the performance of this new approach, and they demonstrate good results in terms of convergence speed and tracking of the reference signal